Summary of No-regret Learning Of Nash Equilibrium For Black-box Games Via Gaussian Processes, by Minbiao Han et al.
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processesby Minbiao Han, Fengxue Zhang,…
Summary of Towards Principled Evaluations Of Sparse Autoencoders For Interpretability and Control, by Aleksandar Makelov et al.
Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Controlby Aleksandar Makelov, George Lange, Neel…
Summary of Tfwt: Tabular Feature Weighting with Transformer, by Xinhao Zhang et al.
TFWT: Tabular Feature Weighting with Transformerby Xinhao Zhang, Zaitian Wang, Lu Jiang, Wanfu Gao, Pengfei…
Summary of The Platonic Representation Hypothesis, by Minyoung Huh et al.
The Platonic Representation Hypothesisby Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip IsolaFirst submitted to arxiv…
Summary of Mambaout: Do We Really Need Mamba For Vision?, by Weihao Yu et al.
MambaOut: Do We Really Need Mamba for Vision?by Weihao Yu, Xinchao WangFirst submitted to arxiv…
Summary of A Survey Of Large Language Models For Graphs, by Xubin Ren et al.
A Survey of Large Language Models for Graphsby Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh…
Summary of A Methodology-oriented Study Of Catastrophic Forgetting in Incremental Deep Neural Networks, by Ashutosh Kumar and Sonali Agarwal et al.
A Methodology-Oriented Study of Catastrophic Forgetting in Incremental Deep Neural Networksby Ashutosh Kumar, Sonali Agarwal,…
Summary of Ctrl: Continuous-time Representation Learning on Temporal Heterogeneous Information Network, by Chenglin Li and Yuanzhen Xie and Chenyun Yu and Lei Cheng and Bo Hu and Zang Li and Di Niu
CTRL: Continuous-Time Representation Learning on Temporal Heterogeneous Information Networkby Chenglin Li, Yuanzhen Xie, Chenyun Yu,…
Summary of Translating Expert Intuition Into Quantifiable Features: Encode Investigator Domain Knowledge Via Llm For Enhanced Predictive Analytics, by Phoebe Jing et al.
Translating Expert Intuition into Quantifiable Features: Encode Investigator Domain Knowledge via LLM for Enhanced Predictive…
Summary of Adakd: Dynamic Knowledge Distillation Of Asr Models Using Adaptive Loss Weighting, by Shreyan Ganguly et al.
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weightingby Shreyan Ganguly, Roshan Nayak,…